Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 49

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f33953802e8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 49

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f33952a6898>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learn_rate = tf.placeholder(tf.float32, name='learn_rate')

    return inputs_real, inputs_z, learn_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """    
    alpha = 0.2
    keep_prob = 0.8
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        l_1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * l_1, l_1)
        relu1= tf.nn.dropout(relu1, keep_prob)
        # 14x14x32
        
        l_2= tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(l_1, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        relu2= tf.nn.dropout(relu2, keep_prob)
        # 7x7x128
        
        l_3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(l_3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        relu3= tf.nn.dropout(relu3, keep_prob)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    keep_prob = 0.8
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        l_1 = tf.layers.dense(z, 2*2*512)
        # Reshape it to start the convolutional stack
        l_1 = tf.reshape(l_1, (-1, 2, 2, 512))
        # x1 = tf.layers.batch_normalization(x1, training=is_train)
        l_1 = tf.maximum(alpha * l_1, l_1)
        l_1= tf.nn.dropout(l_1, keep_prob)
        # 4x4x512 now
        
        l_2 = tf.layers.conv2d_transpose(l_1, 256, 5, strides=2, padding='valid')
        l_2 = tf.layers.batch_normalization(l_2, training=is_train)
        l_2 = tf.maximum(alpha * l_2, l_2)
        l_2= tf.nn.dropout(l_2, keep_prob)
        # 16x16x256 now
        l_3 = tf.layers.conv2d_transpose(l_2, 128, 5, strides=2, padding='same')
        l_3 = tf.layers.batch_normalization(l_3, training=is_train)
        l_3 = tf.maximum(alpha * l_3, l_3)
        l_3= tf.nn.dropout(l_3, keep_prob)
        # Output layer
        logits = tf.layers.conv2d_transpose(l_3, out_channel_dim, 5, strides=2, padding='same')
        # 32x32x3 now
        
        #logits = tf.slice(logits, [0, 2, 2, 0], [-1, 28, 28, -1])
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    
    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    print_every = 100
    steps = 0

    
    # Builds the model
    input_images, input_z, l_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, l_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                
                # Scales the images from -1 to 1
                batch_images = batch_images * 2
                
                # Samples z for the generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run the optimizers
                _ = sess.run(d_opt, feed_dict={input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                
                
                if steps % print_every == 0:
                    # Calculates the loss and prints them
                    train_loss_d = d_loss.eval({input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                    train_loss_g = g_loss.eval({input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                      "Discriminator Loss: {:.4f}...".format(train_loss_d),
                      "Generator Loss: {:.4f}".format(train_loss_g))
                
                    # Shows the current output of the generator
                    show_generator_output(sess, 50, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.2355... Generator Loss: 1.0910
Epoch 1/2... Discriminator Loss: 1.1851... Generator Loss: 1.2030
Epoch 1/2... Discriminator Loss: 1.1771... Generator Loss: 0.9313
Epoch 1/2... Discriminator Loss: 1.2282... Generator Loss: 1.0896
Epoch 1/2... Discriminator Loss: 1.4262... Generator Loss: 0.9243
Epoch 1/2... Discriminator Loss: 1.2604... Generator Loss: 0.9055
Epoch 1/2... Discriminator Loss: 1.3181... Generator Loss: 1.1632
Epoch 1/2... Discriminator Loss: 1.2793... Generator Loss: 0.8567
Epoch 1/2... Discriminator Loss: 1.3153... Generator Loss: 1.0784
Epoch 2/2... Discriminator Loss: 1.3728... Generator Loss: 0.9377
Epoch 2/2... Discriminator Loss: 1.3188... Generator Loss: 0.9111
Epoch 2/2... Discriminator Loss: 1.3734... Generator Loss: 0.7822
Epoch 2/2... Discriminator Loss: 1.2302... Generator Loss: 0.8814
Epoch 2/2... Discriminator Loss: 1.2666... Generator Loss: 0.9961
Epoch 2/2... Discriminator Loss: 1.3000... Generator Loss: 0.9322
Epoch 2/2... Discriminator Loss: 1.3584... Generator Loss: 0.9256
Epoch 2/2... Discriminator Loss: 1.3517... Generator Loss: 1.0626
Epoch 2/2... Discriminator Loss: 1.2931... Generator Loss: 0.9772

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.4301... Generator Loss: 0.8265
Epoch 1/1... Discriminator Loss: 1.4000... Generator Loss: 0.8908
Epoch 1/1... Discriminator Loss: 1.3036... Generator Loss: 0.8585
Epoch 1/1... Discriminator Loss: 1.3210... Generator Loss: 0.8080
Epoch 1/1... Discriminator Loss: 1.4011... Generator Loss: 0.9083
Epoch 1/1... Discriminator Loss: 1.4102... Generator Loss: 0.8622
Epoch 1/1... Discriminator Loss: 1.3254... Generator Loss: 0.8751
Epoch 1/1... Discriminator Loss: 1.3839... Generator Loss: 0.8713
Epoch 1/1... Discriminator Loss: 1.3747... Generator Loss: 0.8471
Epoch 1/1... Discriminator Loss: 1.2976... Generator Loss: 0.8445
Epoch 1/1... Discriminator Loss: 1.4393... Generator Loss: 0.8694
Epoch 1/1... Discriminator Loss: 1.3349... Generator Loss: 0.9386
Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.8829
Epoch 1/1... Discriminator Loss: 1.3089... Generator Loss: 0.8583
Epoch 1/1... Discriminator Loss: 1.3774... Generator Loss: 0.8692
Epoch 1/1... Discriminator Loss: 1.2953... Generator Loss: 1.0001
Epoch 1/1... Discriminator Loss: 1.2929... Generator Loss: 0.8744
Epoch 1/1... Discriminator Loss: 1.3665... Generator Loss: 0.9190
Epoch 1/1... Discriminator Loss: 1.4063... Generator Loss: 0.8581
Epoch 1/1... Discriminator Loss: 1.2571... Generator Loss: 0.8619
Epoch 1/1... Discriminator Loss: 1.3200... Generator Loss: 0.8076
Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.8212
Epoch 1/1... Discriminator Loss: 1.3730... Generator Loss: 0.8065
Epoch 1/1... Discriminator Loss: 1.3096... Generator Loss: 0.9281
Epoch 1/1... Discriminator Loss: 1.3380... Generator Loss: 0.8484
Epoch 1/1... Discriminator Loss: 1.2303... Generator Loss: 0.9583
Epoch 1/1... Discriminator Loss: 1.4601... Generator Loss: 0.7628
Epoch 1/1... Discriminator Loss: 1.3590... Generator Loss: 0.8426
Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.8444
Epoch 1/1... Discriminator Loss: 1.3308... Generator Loss: 0.9291
Epoch 1/1... Discriminator Loss: 1.3553... Generator Loss: 0.9050
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.8957
Epoch 1/1... Discriminator Loss: 1.3960... Generator Loss: 0.8669
Epoch 1/1... Discriminator Loss: 1.3265... Generator Loss: 0.8154
Epoch 1/1... Discriminator Loss: 1.3547... Generator Loss: 0.8117
Epoch 1/1... Discriminator Loss: 1.2862... Generator Loss: 0.8408
Epoch 1/1... Discriminator Loss: 1.4204... Generator Loss: 0.8181
Epoch 1/1... Discriminator Loss: 1.3388... Generator Loss: 0.7967
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.8548
Epoch 1/1... Discriminator Loss: 1.3916... Generator Loss: 0.8637
Epoch 1/1... Discriminator Loss: 1.3222... Generator Loss: 0.8987
Epoch 1/1... Discriminator Loss: 1.3646... Generator Loss: 0.8585
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.8532
Epoch 1/1... Discriminator Loss: 1.3458... Generator Loss: 0.8838
Epoch 1/1... Discriminator Loss: 1.3408... Generator Loss: 0.8373
Epoch 1/1... Discriminator Loss: 1.3290... Generator Loss: 0.7978
Epoch 1/1... Discriminator Loss: 1.2775... Generator Loss: 0.9043
Epoch 1/1... Discriminator Loss: 1.3898... Generator Loss: 0.8304
Epoch 1/1... Discriminator Loss: 1.3681... Generator Loss: 0.8712
Epoch 1/1... Discriminator Loss: 1.3082... Generator Loss: 0.8101
Epoch 1/1... Discriminator Loss: 1.2872... Generator Loss: 0.8116
Epoch 1/1... Discriminator Loss: 1.3403... Generator Loss: 0.8887
Epoch 1/1... Discriminator Loss: 1.3336... Generator Loss: 0.8382
Epoch 1/1... Discriminator Loss: 1.3910... Generator Loss: 0.8181
Epoch 1/1... Discriminator Loss: 1.3369... Generator Loss: 0.8202
Epoch 1/1... Discriminator Loss: 1.2709... Generator Loss: 0.9006
Epoch 1/1... Discriminator Loss: 1.3338... Generator Loss: 0.8338
Epoch 1/1... Discriminator Loss: 1.2944... Generator Loss: 0.8812
Epoch 1/1... Discriminator Loss: 1.3761... Generator Loss: 0.8124
Epoch 1/1... Discriminator Loss: 1.3440... Generator Loss: 0.8514
Epoch 1/1... Discriminator Loss: 1.3353... Generator Loss: 0.8196
Epoch 1/1... Discriminator Loss: 1.3945... Generator Loss: 0.8075
Epoch 1/1... Discriminator Loss: 1.3992... Generator Loss: 0.8219
Epoch 1/1... Discriminator Loss: 1.4008... Generator Loss: 0.7629
Epoch 1/1... Discriminator Loss: 1.3829... Generator Loss: 0.8635
Epoch 1/1... Discriminator Loss: 1.3661... Generator Loss: 0.8259
Epoch 1/1... Discriminator Loss: 1.3841... Generator Loss: 0.8851
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.8206
Epoch 1/1... Discriminator Loss: 1.3534... Generator Loss: 0.9004
Epoch 1/1... Discriminator Loss: 1.3841... Generator Loss: 0.8393
Epoch 1/1... Discriminator Loss: 1.3477... Generator Loss: 0.8816
Epoch 1/1... Discriminator Loss: 1.2890... Generator Loss: 0.8424
Epoch 1/1... Discriminator Loss: 1.2819... Generator Loss: 0.8778
Epoch 1/1... Discriminator Loss: 1.3324... Generator Loss: 0.8461
Epoch 1/1... Discriminator Loss: 1.2746... Generator Loss: 0.8009
Epoch 1/1... Discriminator Loss: 1.3334... Generator Loss: 0.8601
Epoch 1/1... Discriminator Loss: 1.3781... Generator Loss: 0.8007
Epoch 1/1... Discriminator Loss: 1.3695... Generator Loss: 0.7891
Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.9096
Epoch 1/1... Discriminator Loss: 1.2952... Generator Loss: 0.8680
Epoch 1/1... Discriminator Loss: 1.2850... Generator Loss: 0.8312
Epoch 1/1... Discriminator Loss: 1.3433... Generator Loss: 0.9138
Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.8070
Epoch 1/1... Discriminator Loss: 1.3417... Generator Loss: 0.7589
Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.8056
Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.8560
Epoch 1/1... Discriminator Loss: 1.3493... Generator Loss: 0.8190
Epoch 1/1... Discriminator Loss: 1.3030... Generator Loss: 0.8237
Epoch 1/1... Discriminator Loss: 1.3169... Generator Loss: 0.8263
Epoch 1/1... Discriminator Loss: 1.3926... Generator Loss: 0.8878
Epoch 1/1... Discriminator Loss: 1.3817... Generator Loss: 0.8710
Epoch 1/1... Discriminator Loss: 1.3081... Generator Loss: 0.8647
Epoch 1/1... Discriminator Loss: 1.3079... Generator Loss: 0.8215
Epoch 1/1... Discriminator Loss: 1.2732... Generator Loss: 0.8433
Epoch 1/1... Discriminator Loss: 1.3791... Generator Loss: 0.8399
Epoch 1/1... Discriminator Loss: 1.2908... Generator Loss: 0.9023

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.